2020
DOI: 10.3390/s20226638
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Damage Identification and Quantification in Beams Using Wigner-Ville Distribution

Abstract: The paper presents the novel method of damage identification and quantification in beams using the Wigner-Ville distribution (WVD). The presented non-parametric method is characterized by high sensitivity to a local stiffness decrease due to the presence of damage, comparable with the sensitivity of the wavelet-based approaches, however the lack of selection of the parameters of the algorithm, like wavelet type and its order, and the possibility of reduction of the boundary effect make this method advantageous… Show more

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Cited by 10 publications
(5 citation statements)
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References 79 publications
(91 reference statements)
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“…For this reason, it can be defined as a bilinear transform. WVD is characterized by scaling and shift properties, like WT, which makes it useful in the context of numerical applications, including identification of faults using the analysed signals [16]. Taking into account the optimal resolution and the momentary spectrum of power density in time and frequency domains, WVD is considered as particularly useful in diagnosing the rotating machines [9,17,25,40].…”
Section: Wigner-ville Distributionmentioning
confidence: 99%
See 1 more Smart Citation
“…For this reason, it can be defined as a bilinear transform. WVD is characterized by scaling and shift properties, like WT, which makes it useful in the context of numerical applications, including identification of faults using the analysed signals [16]. Taking into account the optimal resolution and the momentary spectrum of power density in time and frequency domains, WVD is considered as particularly useful in diagnosing the rotating machines [9,17,25,40].…”
Section: Wigner-ville Distributionmentioning
confidence: 99%
“…Z tego powodu można ją określić jako transformatę biliniową. WVD charakteryzuje się własnościami skalowania i przesunięcia, podobnie jak WT, co czyni ją przydatną w kontekście zastosowań numerycznych, w tym identyfikacji uszkodzeń za pomocą analizowanych sygnałów [16]. Biorąc pod uwagę optymalną rozdzielczość i widmo chwilowe gęstości mocy w dziedzinach czasu i częstotliwości, WVD jest uznawana za szczególnie przydatną w diagnozowaniu maszyn wirujących [9,17,25,40].…”
Section: Dystrybucja Wigner-villeunclassified
“…The WV distribution analysis is widely used in structural non-stationary signal analysis, fault condition monitoring, damage detection, etc. [ 25 , 26 , 27 , 28 ] Levy et al [ 25 , 26 ] proposed a new WV distribution technique-based time-frequency metric for the characterization of frequency sources, which could be used to solve problems of amplitude fluctuations, phase noise, and frequency instabilities caused by the physical mechanisms of such as the oscillators, clocks, etc. Singru et al [ 27 ] examined an experimental method for condition monitoring of bearing failures by utilizing experimental vibration signatures.…”
Section: Introductionmentioning
confidence: 99%
“…It showed that the WV distribution analysis is more accurate and effective in both time and frequency domain numerical characterization of the obtained vibration signals than other methods, such as FFT. Andrzej [ 28 ] put forward a novel non-parametric damage identification algorithm based on the WV distribution, which exhibits high precision of localization and quantification in damage detection. It also revealed that the WV distribution analysis possesses the advantage that there is no need for parameter selection in the algorithm such as wavelet type and its order, as well as the possibility to reduce the boundary impact.…”
Section: Introductionmentioning
confidence: 99%
“…Another problem appearing during the application of WTs is the presence of the boundary effect, causing a significant increase of the resulting wavelet coefficients in the vicinity of borders of a tested structure. To overcome these deficiencies, some non-parametric methods were applied for damage identification, including cross-correlation [ 35 ], Vigner-Wille distribution [ 36 ], and S-transform [ 37 ]. However, a problem with properly filtering out the damage signatures from processed mode shapes or curvatures remains open in the area of structural damage identification.…”
Section: Introductionmentioning
confidence: 99%